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Make data-driven decisions with statistical rigor
The claim we test
Standardized evidence
Probability of extreme data
Detecting true effects
Hypothesis testing is the cornerstone of data-driven decision making. Learn to test claims systematically, control error rates, and draw valid conclusions from data. From medical trials to A/B testing, master the tools that power modern research and industry.
Essential hypothesis testing concepts
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Build formulas step-by-step
Master hypothesis testing formulas by building them interactively. Click on each part to understand why it's there and how it works.
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Core concepts and framework
Systematic approach to testing claims with data. Build intuition for null/alternative hypotheses, test statistics, and p-values.
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One-tailed vs two-tailed tests
Learn when to use different types of tests. Understand directional vs non-directional hypotheses and their implications.
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Type I/II errors and power analysis
Balance the risks of false positives and false negatives. Learn to calculate and optimize statistical power.
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Z-test fundamentals
How do we test a claim about an average when we know the population's variability? Master the z-test through interactive examples.
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The practical t-test
The realistic scenario: testing means without knowing population variance. Master the t-test and its applications.
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Testing percentage claims
Is the true proportion different from what's claimed? Learn to test hypotheses about percentages and rates.
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Before vs after comparisons
Compare matched pairs: before/after treatment, twin studies, repeated measures. Learn when pairing increases power.
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Comparing independent groups
Compare two independent groups: treatment vs control, method A vs B. Master pooled and Welch's t-tests.
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Comparing success rates
Is treatment A's success rate higher than B's? Compare proportions between groups with proper statistical tests.
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Start with fundamentals to understand the core concepts
Apply concepts to real-world data to reinforce understanding
Learn when and how to apply each concept effectively
Additional interactive visualizations and games to reinforce your understanding. These bonus components provide hands-on practice with key concepts.
Hands-on learning experiences
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Coin fairness detective game
Play detective: determine if a coin is fair or biased through interactive experimentation. Build intuition for hypothesis testing concepts.
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Chi-square test with dice
Watch evidence accumulate against the null hypothesis. Interactive demonstration using dice rolls and chi-square testing.
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Visual error trade-offs
Explore the trade-off between Type I and Type II errors through interactive visualization. See how changing α affects β.
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What p-values really mean
Demystify p-values with interactive visualizations. Understand what they tell us and, more importantly, what they don't.
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One vs two-tailed comparison
Interactive comparison of one-tailed and two-tailed tests. See how the choice affects power and conclusions.
Key topics:
Start with fundamentals to understand the core concepts
Apply concepts to real-world data to reinforce understanding
Learn when and how to apply each concept effectively